%0 Conference Proceedings %T IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces %A Marchisio, Kelly %A Verma, Neha %A Duh, Kevin %A Koehn, Philipp %Y Goldberg, Yoav %Y Kozareva, Zornitsa %Y Zhang, Yue %S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates %F marchisio-etal-2022-isovec %X The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces—their degree of “isomorphism.” We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global measures of isomorphism directly into the skipgram loss function, successfully increasing the relative isomorphism of trained word embedding spaces and improving their ability to be mapped to a shared cross-lingual space. The result is improved bilingual lexicon induction in general data conditions, under domain mismatch, and with training algorithm dissimilarities. We release IsoVec at https://github.com/kellymarchisio/isovec. %R 10.18653/v1/2022.emnlp-main.404 %U https://aclanthology.org/2022.emnlp-main.404 %U https://doi.org/10.18653/v1/2022.emnlp-main.404 %P 6019-6033